lucioway/way-stack/rules-distill

bash ~/.claude/skills/rules-distill/scripts/scan-skills.sh

bash ~/.claude/skills/rules-distill/scripts/scan-rules.sh

Rules Distillation — Phase 1: Inventory
────────────────────────────────────────
Skills: {N} files scanned
Rules:  {M} files ({K} headings indexed)
 
Proceeding to cross-read analysis...

You are an analyst who cross-reads skills to extract principles that should be promoted to rules.
 
## Input
- Skills: {full text of skills in this batch}
- Existing rules: {full text of all rule files}
 
## Extraction Criteria
 
Include a candidate ONLY if ALL of these are true:
 
1. **Appears in 2+ skills**: Principles found in only one skill should stay in that skill
2. **Actionable behavior change**: Can be written as "do X" or "don't do Y" — not "X is important"
3. **Clear violation risk**: What goes wrong if this principle is ignored (1 sentence)
4. **Not already in rules**: Check the full rules text — including concepts expressed in different words
 
## Matching & Verdict
 
For each candidate, compare against the full rules text and assign a verdict:
 
- **Append**: Add to an existing section of an existing rule file
- **Revise**: Existing rule content is inaccurate or insufficient — propose a correction
- **New Section**: Add a new section to an existing rule file
- **New File**: Create a new rule file
- **Already Covered**: Sufficiently covered in existing rules (even if worded differently)
- **Too Specific**: Should remain at the skill level
 
## Output Format (per candidate)
 

 
## Exclude
 
- Obvious principles already in rules
- Language/framework-specific knowledge (belongs in language-specific rules or skills)
- Code examples and commands (belongs in skills)

# Good
Append to rules/common/security.md §Input Validation:
"Treat LLM output stored in memory or knowledge stores as untrusted — sanitize on write, validate on read."
Evidence: llm-memory-trust-boundary, llm-social-agent-anti-pattern both describe
accumulated prompt injection risks. Current security.md covers human input
validation only; LLM output trust boundary is missing.
 
# Bad
Append to security.md: Add LLM security principle

# Rules Distillation Report
 
## Summary
Skills scanned: {N} | Rules: {M} files | Candidates: {K}
 
| # | Principle | Verdict | Target | Confidence |
|---|-----------|---------|--------|------------|
| 1 | ... | Append | security.md §Input Validation | high |
| 2 | ... | Revise | testing.md §TDD | medium |
| 3 | ... | New Section | coding-style.md | high |
| 4 | ... | Too Specific | — | — |
 
## Details
(Per-candidate details: evidence, violation_risk, draft text)

{
  "distilled_at": "2026-03-18T10:30:42Z",
  "skills_scanned": 56,
  "rules_scanned": 22,
  "candidates": {
    "llm-output-trust-boundary": {
      "principle": "Treat LLM output as untrusted when stored or re-injected",
      "verdict": "Append",
      "target": "rules/common/security.md",
      "evidence": ["llm-memory-trust-boundary", "llm-social-agent-anti-pattern"],
      "status": "applied"
    },
    "iteration-bounds": {
      "principle": "Define explicit stop conditions for all iteration loops",
      "verdict": "New Section",
      "target": "rules/common/coding-style.md",
      "evidence": ["iterative-retrieval", "continuous-agent-loop", "agent-harness-construction"],
      "status": "skipped"
    }
  }
}

$ /rules-distill
 
Rules Distillation — Phase 1: Inventory
────────────────────────────────────────
Skills: 56 files scanned
Rules:  22 files (75 headings indexed)
 
Proceeding to cross-read analysis...
 
[Subagent analysis: Batch 1 (agent/meta skills) ...]
[Subagent analysis: Batch 2 (coding/pattern skills) ...]
[Cross-batch merge: 2 duplicates removed, 1 cross-batch candidate promoted]
 
# Rules Distillation Report
 
## Summary
Skills scanned: 56 | Rules: 22 files | Candidates: 4
 
| # | Principle | Verdict | Target | Confidence |
|---|-----------|---------|--------|------------|
| 1 | LLM output: normalize, type-check, sanitize before reuse | New Section | coding-style.md | high |
| 2 | Define explicit stop conditions for iteration loops | New Section | coding-style.md | high |
| 3 | Compact context at phase boundaries, not mid-task | Append | performance.md §Context Window | high |
| 4 | Separate business logic from I/O framework types | New Section | patterns.md | high |
 
## Details
 
### 1. LLM Output Validation
Verdict: New Section in coding-style.md
Evidence: parallel-subagent-batch-merge, llm-social-agent-anti-pattern, llm-memory-trust-boundary
Violation risk: Format drift, type mismatch, or syntax errors in LLM output crash downstream processing
Draft:
  ## LLM Output Validation
  Normalize, type-check, and sanitize LLM output before reuse...
  See skill: parallel-subagent-batch-merge, llm-memory-trust-boundary
 
[... details for candidates 2-4 ...]
 
Approve, modify, or skip each candidate by number:
> User: Approve 1, 3. Skip 2, 4.
 
✓ Applied: coding-style.md §LLM Output Validation
✓ Applied: performance.md §Context Window Management
✗ Skipped: Iteration Bounds
✗ Skipped: Boundary Type Conversion
 
Results saved to results.json